Biology subjects

Prado, M.

Publications and source records attributed to Prado, M..

9 recordsLinked to original sources

bioRxiv2025

Integrative Multi-Omics Analysis Reveals Stress-Specific Molecular Architectures in Soybean under Drought and Rust Infection

Asian soybean rust (ASR), caused by Phakopsora pachyrhizi, represents a major constraint to soybean cultivation, with yield losses approaching 90% in the absence of effective control strategies. When coupled with the increasing incidence of drought driven by climate change, the co-occurrence of these biotic and abiotic pressures imposes a complex challenge for crop resilience. In this study, we explored the molecular responses of soybean (Glycine max) to concurrent water limitation and ASR infection through an integrative analysis of transcriptomic and metabolomic datasets. To capture both linear and conditional relationships among molecular features, we employed Weighted Gene Co-expression Network Analysis (WGCNA) alongside Copula Graphical Models (CGMs). WGCNA identified 17 gene co-expression modules exhibiting significant correlations with 27 annotated metabolites. Among these, abscisic acid showed consistent associations with drought-responsive modules enriched in central metabolic pathways and transcription factors such as Dof and bHLH. In contrast, modules linked to fungal infection were correlated with dipeptides and D-galacturonic acid, implicating early defense signaling and cell wall remodeling. The CGM framework further revealed sparse, condition-specific networks of differentially expressed genes and metabolites directly associated with each stressor, including genes encoding a dirigent-like protein, pentatricopeptide repeat proteins, and a nucleoredoxin, as well as metabolites such as inosine, Epi-dihydrophaseic acid and 2-oxoadipic acid. Notably, no gene or metabolite was found to be directly responsive to both stresses, underscoring the modular and stress-specific architecture of soybean defense. Together, these results highlight a hierarchical regulatory structure and demonstrate the value of combining correlation-based and dependency-driven models to identify candidate targets for multi-stress resilience breeding.

plant biology↗

bioRxiv2025

STIP1/HOP Promotes the Formation of Cytotoxic α-Synuclein Oligomers

The accumulation of alpha-synuclein (a-Syn) as toxic oligomers, and subsequently in Lewy bodies, is a pathological hallmark of Parkinsons disease (PD) and other synucleinopathies. Molecular chaperones and co-chaperones are expected to act in concert to maintain physiological activities of proteins, including a-Syn, but in neurodegeneration this process can become mal-adaptive. Transcript levels of Stress inducible phosphoprotein 1 (STIP1), a co-chaperone of Hsp90/Hsp70, are elevated in brain samples from PD patients. In synucleinopathy mouse models, STIP1 has unexpected bidirectional effects on a-Syn, with overexpression of STIP1 aggravating a-Syn toxicity, whereas knockdown of STIP1 improves toxicity and behavioural phenotypes. However, it is unclear how STIP1 enhances the toxicity of a-Syn. Here we unravel the mechanisms by which the direct interaction between STIP1/HOP and a-Syn regulates the neurotoxicity of a-Syn. Specifically, two binding motifs in the C-terminus of a-Syn directly interact with the TPR2A domain of STIP1/HOP in a dynamic manner, competing for a shared interface on TPR2A. Binding of STIP1/HOP to a-Syn attenuates the formation of a-Syn fibrils while promoting the accumulation of high molecular weight amorphous a-Syn species. Samples of a-Syn aggregated in the presence of STIP1/HOP contain significantly more A11-positive oligomeric species and cause a greater reduction in cell viability than a-Syn aggregated in the absence of STIP1/HOP in neuronal cells. Our results provide a mechanism by which the direct interaction between STIP1/HOP and the C-terminus of a-Syn promotes the formation of cytotoxic, non-amyloidogenic, high molecular weight a-Syn species. Our model offers an explanation for the unexpected pathological link between STIP1 and a-Syn toxicity, thus opening new therapeutic avenues for the treatment of synucleinopathies.

biochemistry↗

bioRxiv2024

Malaria parasite HOPS/CORVET complexes are critical for endocytosis and invasion organelles function

The tethering complexes HOPS/CORVET are central for vesicular fusion through the eukaryotic endolysosomal system, but the functions of these complexes in the intracellular development of malaria parasites are unknown. Here we show that early inactivation of core HOPS/CORVET complex subunits in Plasmodium falciparum leads to developmental arrest and accumulation of cytosolic vesicles, indicating a role of HOPS/CORVET in parasite endocytosis and fusion of endosomes to the digestive vacuole membrane. Late inactivation of the core HOPS/CORVET subunits led to the mislocalization of luminal rhoptry and microneme proteins, and to a severe defect in merozoite invasion. Ultra-expansion microscopy revealed a reduced rhoptry volume and the accumulation of numerous vesicles, further supporting a role of HOPS/CORVET in protein trafficking to the apical organelles. Malaria parasites have therefore repurposed HOPS/CORVET to perform dual functions consistent with a canonical endocytosis pathway for delivery of host cell material to the digestive vacuole in trophozoite stages and a parasite specific role in trafficking of protein cargo to the apical organelles required for invasion in schizont stages.

cell biology↗

bioRxiv2024

The GenPPI tool enhanced Protein Interaction Network Generation with Machine Learning-Based Protein Similarity Inference

AbstractO_ST_ABSBackgroundC_ST_ABSComputational prediction of protein-protein interactions (PPIs) is crucial for understanding cell biology and drug development, offering an alternative to costly experimental methods. The original GenPPi software advanced ab initio PPI network prediction from bacterial genomes but was limited by its reliance on high sequence similarity. This work introduces GenPPi 1.5 to enhance these predictive capabilities. ResultsGenPPi 1.5 incorporates a Random Forest (RF) algorithm, trained on 60 biophysical features from amino acid propensity indices, to classify protein similarity even in low sequence identity scenarios (targeting >65% identity). To manage computational complexity from the increased interactions generated by the RF model, especially in extensive conserved phylogenetic profiles, we developed and integrated the Reduced Interaction Sampling (RIS) algorithm. RIS stochastically samples interactions within these profiles, optimizing performance for complete genome analysis. Extensive simulations across various configurations validated the methodology. RF integration significantly broadened GenPPis predictive power; application to Buchnera aphidicola showed up to 62% overlap with STRING database interactions. Analysis of RIS demonstrated that while introducing some randomness, critical node identification remains robust, particularly for Top N values[≥] 100, indicating minimal compromise to network integrity. ConclusionThe combination of Machine Learning (RF) and the RIS algorithm in GenPPi 1.5 represents a significant advancement. It overcomes the highsimilarity dependency of the previous version while efficiently handling complex genomes. GenPPi 1.5 provides a robust and scalable alignment-free PPI prediction solution, enabling users to train custom models tailored to specific genomic contexts. GenPPi is freely available on our website https://genppi.facom.ufu.br/, its source code is hosted on GitHub https://github.com/santosardr/genppi, and it can be easily installed via the Python Package Index using the command pip install genppipy.

bioinformatics↗

bioRxiv2024

Optimizing multi-environment trials in the US Rice belt via smart-climate-soil prediction based-models and economic importance

Rice breeding programs globally have worked to release increasingly productive and climate-smart cultivars, but the genetic gains have been limited for some reasons. One is the capacity for field phenotyping, which presents elevated costs and an unclear approach to defining the number and allocation of multi-environmental trials (MET). To address this challenge, we used soil information and ten years of historical weather data from the USA rice belt, which was translated into rice response based on the rice cardinal temperatures and crop stages. Next, we eliminated those highly correlated Environmental Covariates (ECs) (>0.95) and applied a supervised algorithm for feature selection using two years of data (2021-22) and 25 genotypes evaluated for grain yield in 18 representative locations in the Southern USA. To test the trials optimization, we performed the joint analysis using prediction-based models in four different scenarios: I) considering trials as non-related, ii) including the environmental relationship matrix calculated from ECs, iii) within clusters; iv) sampling one location per cluster. Finally, we weigh the trials allocation considering the counties economic importance and the environmental group to which they belong. Our findings show that eight ECs explained 58% of grain yield variation across sites and 53% of the observed GxE. Moreover, it is possible to reduce 28% the number of locations without significant loss in accuracy. Furthermore, the US Rice belt comprises four clusters, with economic importance varying from 13 to 45%. These results will help us better allocate trials in advance and reduce costs without penalizing accuracy.

genetics↗

bioRxiv2024

Genotyping-By-Sequencing and DNA array for genomic prediction in soybean oil composition

Soybean oil is intended for various purposes, such as cooking oil and biodiesel. The oil composition changes the shelf life, palatability, and how healthy this oil is for the human diet. Genomic selection jointly uses these traits, phenotypes, and markers from one of the available genotyping platforms to increase genetic gain over time. This study aims to evaluate the impact of different genotyping platforms, DNA arrays, and Genotyping-by-Sequencing (GBS) on genomic selection in relation to the composition of fatty acids in soybean oil and total oil content. We used different quality control parameters, such as heterozygote rate, minor allele frequency, and missing data rate in ten combinations, and two prediction models, BayesB and BRR. To compare the impact of the genotyping approaches, we calculated the principal components analysis from the kinship matrices, the SNP density, and the traits prediction accuracies for each approach. Principal component analysis showed that the DNA array explained better the population genetic architecture. On the other hand, prediction accuracies varied between the different genotyping platforms and only GBS was affected under different quality control parameters. Although the DNA array has important and well-studied polymorphisms for soybeans and is stable, it also has ascertainment bias. GBS, although not stable and requires more robust quality control, can discover alleles specific to the population under study. As soybean oil is used for different functions and the fatty acid profiles are different for each objective, the work constitutes a critical study and direction for improving the composition of soybean oil.

genetics↗

bioRxiv2024

Complementary approaches to dissect late leaf rust resistance in an interspecific raspberry population

Over the last ten years, global raspberry production has increased by 47.89%, based on the red species (Rubus idaeus). However, the black raspberry species (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus, to which the red ones are susceptible. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in the crop in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late rust in raspberries. For that, we used an interspecific diversity panel between the cited above species, two different statistical approaches to associate the phenotypes to the markers (GWAS and copula graphical models), and two phenotyping methodologies from the first to the seventeenth day after inoculation (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect possibly controls the resistance to late leaf rust, as both GWAS and the network suggested the same marker. Furthermore, a candidate defense-related gene cluster is close to this marker. Finally, the best stage to evaluate for disease severity is thirteen days after inoculation, confirmed by both traditional and high-throughput phenotyping. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different regions complementing the genetic control comprehension.

genetics↗

bioRxiv2020

Hsp90 and its co-chaperone Sti1 control TDP-43 misfolding and toxicity

Protein misfolding is a central feature of most neurodegenerative diseases. Molecular chaperones can modulate the toxicity associated with protein misfolding, but it remains elusive which molecular chaperones and co-chaperones interact with specific misfolded proteins. TDP-43 misfolding and inclusion formation is a hallmark of amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases. Using yeast and mammalian neuronal cells we find that Hsp90 and its co-chaperones have a strong capacity to alter TDP-43 misfolding, inclusion formation, aggregation, and cellular toxicity. Our data also demonstrate that impaired Hsp90 function sensitizes cells to TDP-43 toxicity. We further show that the co-chaperone Sti1 specifically interacts with and modulates TDP-43 toxicity in a dose-dependent manner. Our study thus uncovers a previously unrecognized tie between Hsp90, Sti1, TDP-43 misfolding, and its cellular toxicity.

cell biology↗

bioRxiv2017

Mapping nonapoptotic caspase activity with a transgenic reporter in mice

The protease caspase-3 is a key mediator of apoptotic programmed cell death. But weak or transient caspase activity can contribute to neuronal differentiation, axonal pathfinding, and synaptic long-term depression. Despite the importance of sublethal, or nonapoptotic, caspase activity in neurodevelopment and neural plasticity, there has been no simple method for mapping and quantifying nonapoptotic caspase activity in rodent brains. We therefore generated a transgenic mouse expressing a highly sensitive and specific fluorescent reporter of caspase activity, with peak signal localized to the nucleus. As a proof of concept, we first obtained evidence that nonapoptotic caspase activity influences neurophysiology in an amygdalar circuit. Then focusing on the amygdala, we were able to quantify a sex-specific persistent elevation in caspase activity in females after restraint stress. This simple in vivo caspase activity reporter will facilitate systems-level studies of apoptotic and nonapoptotic phenomena in behavioral and pathological models. Significance StatementCaspase-3 is an enzyme that can cause cell death when highly active but can also perform important cellular functions, such as maturation and structural changes, when only weakly or transiently active. Despite the importance of this nonlethal type of caspase activity, there is no straightforward method to measure it in live rodents. We therefore developed mice that have a fluorescent reporter that is sensitive enough to detect nonlethal caspase activity. Surprisingly, we found that weak caspase activity influences the synchrony of neuronal firing across different brain regions. We also observed increased caspase activity in female mice after severe stress. This simple caspase activity reporter can subserve multiple applications in behavior and pathology research.

neuroscience↗